{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ede095b1",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Model Fitting\n",
    "\n",
    "### Michael J. Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e15b023",
   "metadata": {},
   "source": [
    "#### Fitting a Model\n",
    "\n",
    "There are two common methods to fit a model, ordinary least squares and maximim likelihood estimation. Here I provide a short description and then demonstrate them together for fitting a parametric Gaussian distribution to a synthetic data set.\n",
    "\n",
    "First, let's define the model and data.\n",
    "\n",
    "* The model parameters, $\\beta$, are trained to the training data, $X_{\\alpha}, \\alpha = 1,\\ldots,n$.\n",
    "* In this example the model is a parametric Gaussian distribution; therefore the model parameters are the mean, $\\mu$, and standard deviation, $\\sigma$.\n",
    "* For this example, the synthetic data are a limited set of independent samples from an 'unknown' Gaussian distributions; therefore, we expect that our model selection should be reasonable, i.e., the data is somewhat Gaussian distributed.\n",
    "\n",
    "##### Ordinary Least Squares Fitting\n",
    "\n",
    "This is a very common method for fitting a model, let's minimize the error between the model predictions and the observations. \n",
    "\n",
    "* We calculated the model predictions, $\\hat{y}_{\\alpha}$, at training data locations, $\\alpha = 1,\\ldots,n$.  \n",
    "* Then we compare the model predictions to the true data observations as the squared error, $\\Delta y_{\\alpha}^2 = \\left(\\hat{y}_{\\alpha} - y_{\\alpha} \\right)^2$, where the model predictions, $\\hat{y}_{\\alpha} = \\hat{f}_{\\beta}(X_{\\alpha})$, are estimated with our estimated model parameters, $\\hat{f}_{\\beta}(X_{\\alpha})$.\n",
    "* We then sum the squared error over all data observations, the sum of squared error (SSE) is $\\sum_{\\alpha=1}^n \\left(\\hat{y}_{\\alpha} - y_{\\alpha} \\right)^2$\n",
    "\n",
    "Now we pose the model parameter estimation problem as an optimization problem to select the model parameters such that we minimize the SSE.\n",
    "\n",
    "\\begin{equation}\n",
    "\\hat{\\beta}^{OLS}= {\\text{arg min}}_{\\beta} \\rightarrow \\left(\\hat{f}_{\\beta}(X_{\\alpha}) - y_{\\alpha} \\right)^2\n",
    "\\end{equation}\n",
    "\n",
    "##### Maximum Likelihood Fitting\n",
    "\n",
    "This is a different way to look at fitting a model. \n",
    "\n",
    "* We calculated the model predictions, $\\hat{y}_{\\alpha}$, at training data locations, $\\alpha = 1,\\ldots,n$.  \n",
    "* Then we compare the model predictions to the true data observations as the squared error, $\\Delta y_{\\alpha}^2 = \\left(\\hat{y}_{\\alpha} - y_{\\alpha} \\right)^2$, where the model predictions, $\\hat{y}_{\\alpha} = \\hat{f}_{\\beta}(X_{\\alpha})$, are estimated with our estimated model parameters, $\\hat{f}_{\\beta}(X_{\\alpha})$.\n",
    "* We then sum the squared error over all data observations, the sum of squared error (SSE) is $\\sum_{\\alpha=1}^n \\left(\\hat{y}_{\\alpha} - y_{\\alpha} \\right)^2$\n",
    "\n",
    "Now we pose the model parameter estimation problem as an optimization problem to select the model parameters such that we minimize the SSE.\n",
    "\n",
    "\\begin{equation}\n",
    "\\hat{\\beta}^{OLS}= {\\text{arg min}}_{\\beta} \\rightarrow \\left(\\hat{f}_{\\beta}(X_{\\alpha}) - y_{\\alpha} \\right)^2\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### Gibbs Sampler for Bivariate Gaussian Distribution\n",
    "\n",
    "Below I build out an interactive Gibbs sampler to sample the bivariate joint Gaussian distribution from only the conditional distributions!\n",
    "\n",
    "#### Load and Configure the Required Libraries\n",
    "\n",
    "The following code loads the required libraries and sets a plotting default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da837ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "seed=73073                                              \n",
    "supress_warnings = False\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.optimize import minimize\n",
    "from scipy.stats import norm                            # Gaussian PDF\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "import seaborn as sns                                   # plot PDF\n",
    "from sklearn.model_selection import train_test_split    # train and test split\n",
    "from sklearn import tree                                # tree program from scikit learn (package for machine learning)\n",
    "from sklearn import metrics                             # measures to check our models\n",
    "import scipy.stats as stats                             #search for neighbours\n",
    "from matplotlib.patches import Rectangle                # build a custom legend\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "import math                                             # sqrt operator\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "cmap = plt.cm.inferno                                   # default color bar, no bias and friendly for color vision defeciency\n",
    "plt.rc('axes', axisbelow=True)                          # grid behind plotting elements\n",
    "if supress_warnings == True:\n",
    "    import warnings                                     # supress any warnings for this demonstration\n",
    "    warnings.filterwarnings('ignore')                  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b57659e",
   "metadata": {},
   "source": [
    "#### Declare Functions\n",
    "\n",
    "The following functions for clean code. \n",
    "\n",
    "* Just a improved grid for the plot.\n",
    "\n",
    "* Gaussian negative log likelihood function modified from [StackExchange](https://stats.stackexchange.com/questions/504004/how-do-we-code-a-maximum-likelihood-fitting-for-a-simple-gaussian-data) solution from jkpate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a333fd85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_grid():\n",
    "    plt.gca().grid(True, which='major',linewidth = 1.0); plt.gca().grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "    \n",
    "def gaussian_negloglike(params):    # Calculate sum negative log likelihood\n",
    "    mu = params[0]; sigma = params[1]\n",
    "    neg_log_likelihood = -1*np.sum(stats.norm.logpdf(X, loc=mu, scale=sigma)) \n",
    "    return neg_log_likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f575fb62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a59e3cb8",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 35; tmean = 0.5; tstdev = 0.1; xmin=0.0; xmax=1.0\n",
    "np.random.seed(seed=seed)                               # set random number seed\n",
    "Xval = np.linspace(xmin,xmax,100)\n",
    "X = np.random.normal(loc=tmean,scale=tstdev,size=n-2); X = np.append(X,[0.8,0.9])\n",
    "X = np.sort(X); CDF = np.arange(1,n+1,1)/(n+1)\n",
    "\n",
    "mean = np.average(X); stdev = np.std(X)\n",
    "\n",
    "mean_ls,stdev_ls = curve_fit(norm.cdf,X,CDF,p0=[0,1],method='lm')[0] \n",
    "\n",
    "mean_ml,stdev_ml = minimize(gaussian_negloglike,x0=[0,1],method='Nelder-Mead').x\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.scatter(np.sort(X),np.arange(1,n+1,1)/(n+1),c='black',s=10,edgecolor='black',zorder=10)\n",
    "plt.title('Copper Distribution via Parameter Estimation')\n",
    "# plt.plot(Xval,stats.norm.cdf(Xval,loc=mean,scale=stdev),c='black',zorder=1,label='Parameter Inference')\n",
    "plt.plot(Xval,stats.norm.cdf(Xval,loc=mean_ls,scale=stdev_ls),c='red',zorder=1,label='OLS')\n",
    "plt.plot(Xval,stats.norm.cdf(Xval,loc=mean_ml,scale=stdev_ml),c='blue',zorder=1,label='Maximum Likelihood')\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylim([0,1]); plt.xlim([0,1.0]); add_grid(); plt.xlabel('CU Grade (g/t)'); plt.ylabel('Cumulative Probability')\n",
    "\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=3.0,top=1.2); plt.show() # set plot size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd0ae7f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4f868438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = 0.5; stdev = 0.124\n",
    "\n",
    "np.random.seed(seed=seed)                               # set random number seed\n",
    "Xval = np.linspace(xmin,xmax,100)\n",
    "\n",
    "CDF_hat = norm.cdf(X,loc = mean,scale=stdev)            # for specified mean and stdev calculate the PDF and CDF\n",
    "pdf_hat = stats.norm.pdf(X,loc=mean,scale=stdev)\n",
    "sq_err = (CDF-CDF_hat)**2                               # calculate the square error of the CDF\n",
    "sse = np.sum(sq_err)\n",
    "prod_like = np.product(pdf_hat)                         # calculate the likelihoof from the PDF\n",
    "neg_log_like = -np.sum(np.log(pdf_hat))\n",
    "\n",
    "mean_OLS,stdev_OLS = curve_fit(norm.cdf,np.sort(X),np.arange(1,n+1,1)/(n+1), p0=[0,1],method='lm')[0] # calculate OLS and error\n",
    "sq_err_OLS = (CDF-norm.cdf(X,loc = mean_OLS,scale=stdev_OLS))**2\n",
    "sse_OLS = np.sum(sq_err_OLS)\n",
    "\n",
    "mean_MLE,stdev_MLE = minimize(gaussian_negloglike,x0=[0,1],method='Nelder-Mead').x # calculate MLE and likelihood\n",
    "pdf_hat_MLE = stats.norm.pdf(X,loc=mean_MLE,scale=stdev_MLE)\n",
    "prod_like_MLE = np.product(pdf_hat_MLE) \n",
    "neg_log_like_MLE = -np.sum(np.log(pdf_hat_MLE))\n",
    "\n",
    "plt.subplot(221)                                         # plot CDF and errors and OLS solution\n",
    "plt.scatter(np.sort(X),np.arange(1,n+1,1)/(n+1),c='black',s=10,edgecolor='black',zorder=10)\n",
    "plt.title('Ordinary Least Squares - Model and Data Error')\n",
    "plt.plot(Xval,stats.norm.cdf(Xval,loc=mean,scale=stdev),c='red',lw=3,label=r'$\\hat{F}_{Cu}(\\alpha)$',zorder=100)\n",
    "plt.plot(Xval,stats.norm.cdf(Xval,loc=mean_OLS,scale=stdev_OLS),c='grey',alpha=0.3,lw=3,label=r'$\\hat{F}_{Cu}^{OLS}(\\alpha)$',zorder=10)\n",
    "for i in range(0,len(X)):\n",
    "    plt.plot([X[i],X[i]],[CDF[i],CDF_hat[i]],color='black',alpha=0.3,zorder=1)\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylim([0,1]); plt.xlim([0,1.0]); add_grid(); plt.xlabel('Copper Grade (g/t)'); plt.ylabel(r'Cumulative Probability, $F_{Cu}$')\n",
    "plt.annotate(r'Current Model: $\\mu = $' + str(np.round(mean,2)),xy=[0.68,0.4],c='red')\n",
    "plt.annotate(r'$\\sigma = $' + str(np.round(stdev,2)),xy=[0.80,0.35],c='red')\n",
    "plt.annotate(r'OLS Solution: $\\mu = $' + str(np.round(mean_OLS,2)),xy=[0.69,0.3],c='black')\n",
    "plt.annotate(r'$\\sigma = $' + str(np.round(stdev_OLS,2)),xy=[0.80,0.25],c='black')\n",
    "\n",
    "plt.subplot(222)                                         # plot error distribution and OLS solution\n",
    "plt.hist(sq_err,color='red',alpha=0.6,edgecolor='darkred',lw=2,bins=np.linspace(0.0,0.1,41),zorder=10)\n",
    "plt.hist(sq_err_OLS,color='grey',alpha=1.0,edgecolor='black',lw=2,bins=np.linspace(0.0,0.1,41),zorder=1)\n",
    "plt.xlabel(r'Squared Error, $\\left( \\hat{F}_{Cu}(\\alpha) - F_{Cu}(\\alpha) \\right)^2$'); plt.ylabel('Frequency'); plt.title('Ordinary Least Squares - Data Error Distribution')\n",
    "add_grid(); plt.xlim([0.0,0.1]); plt.ylim([0,30])\n",
    "plt.annotate(r'Minimize: $\\sum_{\\alpha=1}^n \\left( \\hat{F}_{Cu}(\\alpha) - F_{Cu}(\\alpha) \\right)^2 = $' + str(np.round(sse,2)),xy=[0.065,25.0],c='red')\n",
    "plt.annotate(r'OLS: $\\sum_{\\alpha=1}^n \\left( \\hat{F}_{Cu}^{OLS}(\\alpha) - F_{Cu}(\\alpha) \\right)^2 = $' + str(np.round(sse_OLS,2)),xy=[0.069,22.0],c='black')\n",
    "\n",
    "plt.subplot(223)                                         # plot data likelihood, PDF and MLE solution\n",
    "plt.scatter(np.sort(X),np.full(len(X),0.03),c='black',s=10,edgecolor='black',zorder=10)\n",
    "plt.title('Copper Distribution via Parameter Estimation')\n",
    "plt.plot(Xval,stats.norm.pdf(Xval,loc=mean,scale=stdev),c='blue',lw=3,label=r'$\\hat{f}_{Cu}(\\alpha)$',zorder=100)\n",
    "plt.plot(Xval,stats.norm.pdf(Xval,loc=mean_MLE,scale=stdev_MLE),c='grey',alpha=0.3,lw=3,label=r'$\\hat{f}_{Cu}^{MLE}(\\alpha)$',zorder=10)\n",
    "for i in range(0,len(X)):\n",
    "    plt.plot([X[i],X[i]],[0.0,pdf_hat[i]],color='black',alpha=0.3,zorder=1)\n",
    "plt.annotate(r'Current Model: $\\mu = $' + str(np.round(mean,2)),xy=[0.68,3.5],c='blue')\n",
    "plt.annotate(r'$\\sigma = $' + str(np.round(stdev,2)),xy=[0.80,3.3],c='blue')\n",
    "plt.annotate(r'MLE Solution: $\\mu = $' + str(np.round(mean_MLE,2)),xy=[0.69,3.1],c='black')\n",
    "plt.annotate(r'$\\sigma = $' + str(np.round(stdev_MLE,2)),xy=[0.80,2.9],c='black')\n",
    "plt.xlabel('Copper Grade (g/t)'); plt.ylabel(r'Likelihood, Density, $f_{Cu}$'); plt.title('Maximum Likelihood - Model and Data Likelihood')\n",
    "add_grid(); plt.xlim([0.0,1.0]); plt.ylim([0.0,4.0]); plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplot(224)                                          # plot data likelihood distribution and MLS solution\n",
    "plt.hist(pdf_hat,color='blue',alpha=0.5,edgecolor='darkblue',lw=2,bins=np.linspace(0.0,4.0,41),orientation='horizontal',zorder=100)\n",
    "plt.hist(pdf_hat_MLE,color='grey',alpha=1.0,edgecolor='black',lw=2,bins=np.linspace(0.0,4.0,41),orientation='horizontal',zorder=10)\n",
    "plt.xlabel('Frequency'); plt.ylabel(r'Likelihood, Density, $f_{Cu}$'); plt.title('Maximum Likelihood - Data Likelihood Distribution')\n",
    "add_grid(); plt.xlim([0.0,10.0]); plt.ylim([0.0,4.0])\n",
    "plt.annotate(r'Maximize: $\\prod_{\\alpha=1}^n \\hat{f}_\\alpha = $' + str(np.round(prod_like,2)),xy=[6.5,3.5],c='blue')\n",
    "plt.annotate(r'MLE: $\\prod_{\\alpha=1}^n \\hat{f}_{Cu}^{MLE}(\\alpha) = $' + str(np.round(prod_like_MLE,2)),xy=[6.9,3.1],c='black')\n",
    "plt.annotate(r'Minimize: $-\\sum_{\\alpha=1}^n log\\left[ \\hat{f}_{Cu}(\\alpha) \\right]  = $' + str(np.round(neg_log_like,2)),xy=[6.5,2.7],c='blue')\n",
    "plt.annotate(r'MLE: $-\\sum_{\\alpha=1}^n \\left( log(\\hat{f}_{Cu}^{MLE}(\\alpha) )   \\right)  = $' + str(np.round(neg_log_like_MLE,2)),xy=[6.9,2.3],c='black')\n",
    " \n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=3.0,top=2.2); plt.show() # set plot size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "24706ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1179180831724761, 0.09591403395906896)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8677ce93",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = widgets.Text(value='                                  Interactive Statistical Model Fitting Demo, Prof. Michael Pyrcz, The University of Texas at Austin',\n",
    "                 layout=Layout(width='750px', height='30px'))\n",
    "\n",
    "mean = widgets.FloatSlider(min=0.0, max = 1.0, value=0.5, step = 0.02, description = '$\\mu$',orientation='horizontal', \n",
    "           style = {'description_width': 'initial'},layout=Layout(width='370px', height='30px'),continuous_update=False)\n",
    "stdev = widgets.FloatSlider(min=0.01, max = 1.0, value=0.1, step = 0.02, description = r'$\\sigma$',orientation='horizontal',\n",
    "           style = {'description_width': 'initial'},layout=Layout(width='370px', height='30px'),continuous_update=False)\n",
    "\n",
    "ui = widgets.HBox([mean,stdev],)\n",
    "ui2 = widgets.VBox([l,ui],)\n",
    "\n",
    "def run_plot(mean,stdev):\n",
    "    np.random.seed(seed=seed)                               # set random number seed\n",
    "    Xval = np.linspace(xmin,xmax,100)\n",
    "    \n",
    "    CDF_hat = norm.cdf(X,loc = mean,scale=stdev)            # for specified mean and stdev calculate the PDF and CDF\n",
    "    pdf_hat = stats.norm.pdf(X,loc=mean,scale=stdev)\n",
    "    sq_err = (CDF-CDF_hat)**2                               # calculate the square error of the CDF\n",
    "    sse = np.sum(sq_err)\n",
    "    prod_like = np.product(pdf_hat)                         # calculate the likelihoof from the PDF\n",
    "    neg_log_like = -np.sum(np.log(pdf_hat))\n",
    "    \n",
    "    mean_OLS,stdev_OLS = curve_fit(norm.cdf,np.sort(X),np.arange(1,n+1,1)/(n+1), p0=[0,1],method='lm')[0] # calculate OLS and error\n",
    "    sq_err_OLS = (CDF-norm.cdf(X,loc = mean_OLS,scale=stdev_OLS))**2\n",
    "    sse_OLS = np.sum(sq_err_OLS)\n",
    "    \n",
    "    mean_MLE,stdev_MLE = minimize(gaussian_negloglike,x0=[0,1],method='Nelder-Mead').x # calculate MLE and likelihood\n",
    "    pdf_hat_MLE = stats.norm.pdf(X,loc=mean_MLE,scale=stdev_MLE)\n",
    "    prod_like_MLE = np.product(pdf_hat_MLE) \n",
    "    neg_log_like_MLE = -np.sum(np.log(pdf_hat_MLE))\n",
    "    \n",
    "    plt.subplot(221)                                         # plot CDF and errors and OLS solution\n",
    "    plt.scatter(np.sort(X),np.arange(1,n+1,1)/(n+1),c='black',s=10,edgecolor='black',zorder=10)\n",
    "    plt.title('Ordinary Least Squares - Model and Data Error')\n",
    "    plt.plot(Xval,stats.norm.cdf(Xval,loc=mean,scale=stdev),c='red',lw=3,label=r'$\\hat{F}_{Cu}(\\alpha)$',zorder=100)\n",
    "    plt.plot(Xval,stats.norm.cdf(Xval,loc=mean_OLS,scale=stdev_OLS),c='grey',alpha=0.3,lw=3,label=r'$\\hat{F}_{Cu}^{OLS}(\\alpha)$',zorder=10)\n",
    "    for i in range(0,len(X)):\n",
    "        plt.plot([X[i],X[i]],[CDF[i],CDF_hat[i]],color='black',alpha=0.3,zorder=1)\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.ylim([0,1]); plt.xlim([0,1.0]); add_grid(); plt.xlabel('Copper Grade (g/t)'); plt.ylabel(r'Cumulative Probability, $F_{Cu}$')\n",
    "    plt.annotate(r'Current Model: $\\mu = $' + str(np.round(mean,2)),xy=[0.68,0.4],c='red')\n",
    "    plt.annotate(r'$\\sigma = $' + str(np.round(stdev,2)),xy=[0.80,0.35],c='red')\n",
    "    plt.annotate(r'OLS Solution: $\\mu = $' + str(np.round(mean_OLS,2)),xy=[0.69,0.3],c='black')\n",
    "    plt.annotate(r'$\\sigma = $' + str(np.round(stdev_OLS,2)),xy=[0.80,0.25],c='black')\n",
    "    \n",
    "    plt.subplot(222)                                         # plot error distribution and OLS solution\n",
    "    plt.hist(sq_err,color='red',alpha=0.6,edgecolor='darkred',lw=2,bins=np.linspace(0.0,0.1,41),zorder=10)\n",
    "    plt.hist(sq_err_OLS,color='grey',alpha=1.0,edgecolor='black',lw=2,bins=np.linspace(0.0,0.1,41),zorder=1)\n",
    "    plt.xlabel(r'Squared Error, $\\left( \\hat{F}_{Cu}(\\alpha) - F_{Cu}(\\alpha) \\right)^2$'); plt.ylabel('Frequency'); plt.title('Ordinary Least Squares - Data Error Distribution')\n",
    "    add_grid(); plt.xlim([0.0,0.1]); plt.ylim([0,30])\n",
    "    plt.annotate(r'Minimize: $\\sum_{\\alpha=1}^n \\left( \\hat{F}_{Cu}(\\alpha) - F_{Cu}(\\alpha) \\right)^2 = $' + str(np.round(sse,2)),xy=[0.065,25.0],c='red')\n",
    "    plt.annotate(r'OLS: $\\sum_{\\alpha=1}^n \\left( \\hat{F}_{Cu}^{OLS}(\\alpha) - F_{Cu}(\\alpha) \\right)^2 = $' + str(np.round(sse_OLS,2)),xy=[0.069,22.0],c='black')\n",
    "    \n",
    "    plt.subplot(223)                                         # plot data likelihood, PDF and MLE solution\n",
    "    plt.scatter(np.sort(X),np.full(len(X),0.03),c='black',s=10,edgecolor='black',zorder=10)\n",
    "    plt.title('Copper Distribution via Parameter Estimation')\n",
    "    plt.plot(Xval,stats.norm.pdf(Xval,loc=mean,scale=stdev),c='blue',lw=3,label=r'$\\hat{f}_{Cu}(\\alpha)$',zorder=100)\n",
    "    plt.plot(Xval,stats.norm.pdf(Xval,loc=mean_MLE,scale=stdev_MLE),c='grey',alpha=0.3,lw=3,label=r'$\\hat{f}_{Cu}^{MLE}(\\alpha)$',zorder=10)\n",
    "    for i in range(0,len(X)):\n",
    "        plt.plot([X[i],X[i]],[0.0,pdf_hat[i]],color='black',alpha=0.3,zorder=1)\n",
    "    plt.annotate(r'Current Model: $\\mu = $' + str(np.round(mean,2)),xy=[0.68,3.5],c='blue')\n",
    "    plt.annotate(r'$\\sigma = $' + str(np.round(stdev,2)),xy=[0.80,3.3],c='blue')\n",
    "    plt.annotate(r'MLE Solution: $\\mu = $' + str(np.round(mean_MLE,2)),xy=[0.69,3.1],c='black')\n",
    "    plt.annotate(r'$\\sigma = $' + str(np.round(stdev_MLE,2)),xy=[0.80,2.9],c='black')\n",
    "    plt.xlabel('Copper Grade (g/t)'); plt.ylabel(r'Likelihood, Density, $f_{Cu}$'); plt.title('Maximum Likelihood - Model and Data Likelihood')\n",
    "    add_grid(); plt.xlim([0.0,1.0]); plt.ylim([0.0,4.0]); plt.legend(loc='upper left')\n",
    "    \n",
    "    plt.subplot(224)                                          # plot data likelihood distribution and MLS solution\n",
    "    plt.hist(pdf_hat,color='blue',alpha=0.5,edgecolor='darkblue',lw=2,bins=np.linspace(0.0,4.0,41),orientation='horizontal',zorder=100)\n",
    "    plt.hist(pdf_hat_MLE,color='grey',alpha=1.0,edgecolor='black',lw=2,bins=np.linspace(0.0,4.0,41),orientation='horizontal',zorder=10)\n",
    "    plt.xlabel('Frequency'); plt.ylabel(r'Likelihood, Density, $f_{Cu}$'); plt.title('Maximum Likelihood - Data Likelihood Distribution')\n",
    "    add_grid(); plt.xlim([0.0,10.0]); plt.ylim([0.0,4.0])\n",
    "    plt.annotate(r'Maximize: $\\prod_{\\alpha=1}^n \\hat{f}_\\alpha = $' + str(np.round(prod_like,2)),xy=[6.5,3.5],c='blue')\n",
    "    plt.annotate(r'MLE: $\\prod_{\\alpha=1}^n \\hat{f}_{Cu}^{MLE}(\\alpha) = $' + str(np.round(prod_like_MLE,2)),xy=[6.9,3.1],c='black')\n",
    "    plt.annotate(r'Minimize: $-\\sum_{\\alpha=1}^n log\\left[ \\hat{f}_{Cu}(\\alpha) \\right]  = $' + str(np.round(neg_log_like,2)),xy=[6.5,2.7],c='blue')\n",
    "    plt.annotate(r'MLE: $-\\sum_{\\alpha=1}^n \\left( log(\\hat{f}_{Cu}^{MLE}(\\alpha) )   \\right)  = $' + str(np.round(neg_log_like_MLE,2)),xy=[6.9,2.3],c='black')\n",
    "     \n",
    "    plt.subplots_adjust(left=0.0,bottom=0.0,right=3.0,top=2.2); plt.show() # set plot size \n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(run_plot, {'mean':mean,'stdev':stdev})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e02f29",
   "metadata": {},
   "source": [
    "### Interactive Statistical Model Fitting Demonstation \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "Change the mean and standard deviation of a Gaussian model and observe the ordinary least squares and maximum likelihood assessment of fit.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* Gaussian Parametric Model: **$\\mu$** - mean, **$\\sigma$** - standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "483fc030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52fc9f6932494d2da348d0d16d896083",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                  Interactive Statistical Model Fitting Demo, Prof.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20c864ebde2f4579ba1eefd291029727",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 4 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui2, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8a62219",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Gauss(x, A, B): \n",
    "    y = A*np.exp(-1*B*x**2) \n",
    "    return y \n",
    "parameters, covariance = curve_fit(stats.norm.cdf(), np.sort(X),np.arange(1,n+1,1)/(n+1)) \n",
    "fit_A = parameters[0] \n",
    "fit_B = parameters[1] \n",
    "  \n",
    "fit_y = Gauss(Xval, fit_A, fit_B) \n",
    "#plt.plot(xdata, ydata, 'o', label='data') \n",
    "plt.plot(Xval, fit_y, '-', label='fit') \n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f872a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "700e0e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e382a4e2",
   "metadata": {},
   "source": [
    "#### Interactive Gibbs Sampler to Sample the Bivariate Gausian Distribution Dashboard\n",
    "\n",
    "Here's a dashboard with a cool visualization for my interactive Gibbs sampler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90be8276",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = widgets.Text(value='                                  Interactive Gibbs Sampler Demo, Prof. Michael Pyrcz, The University of Texas at Austin',\n",
    "                 layout=Layout(width='750px', height='30px'))\n",
    "\n",
    "nsample = widgets.IntSlider(min=1, max = 101, value=10, step = 1, description = '$n_{sample}$',orientation='horizontal', \n",
    "           style = {'description_width': 'initial'},layout=Layout(width='370px', height='30px'),continuous_update=False)\n",
    "rho = widgets.FloatSlider(min=-1.0, max = 1.0, value=0.7, step = 0.1, description = r'$\\rho_{X_1,X_2}$',orientation='horizontal',\n",
    "           style = {'description_width': 'initial'},layout=Layout(width='370px', height='30px'),continuous_update=False)\n",
    "\n",
    "ui = widgets.HBox([nsample,rho],)\n",
    "ui2 = widgets.VBox([l,ui],)\n",
    "\n",
    "def run_plot(nsample,rho):\n",
    "    mu1 = 0.0; sig1 = 1.0; mu2 = 0.0; sig2 = 1.0; seed = 73073; nc = 200\n",
    "    \n",
    "    L = nsample\n",
    "    np.random.seed(seed=seed)\n",
    "    x1 = np.zeros(L); x2 = np.zeros(L); x = np.linspace(-3,3,nc)\n",
    "    \n",
    "    x1[0] = np.random.rand(1) * 6.0 - 3.0; x2[0] = np.random.rand(1) * 6.0 - 3.0; \n",
    "    \n",
    "    plt.subplot(111)\n",
    "    plt.scatter(x1[0],x2[0],color='grey',edgecolor='black',s=15,zorder=4)\n",
    "    \n",
    "    case = 0\n",
    "    \n",
    "    for l in range(1,L):\n",
    "        if case == 0: # update x2\n",
    "            x1[l] = x1[l-1]\n",
    "            lmu = mu2 + rho * (sig2/sig1) * (x1[l] - mu1); lstd = 1 - rho**2\n",
    "            x2[l] = np.random.normal(loc = lmu,scale = lstd,size = 1)\n",
    "            case = 1\n",
    "            plt.scatter(x1[l],x2[l],color='blue',edgecolor='black',s=15,alpha=1.0,zorder=100)\n",
    "            plt.plot([x1[l-1],x1[l]],[x2[l-1],x2[l]],color='black',lw=1,alpha = max((l-(L-20))/20,0),zorder=4)\n",
    "            plt.plot([x1[l-1],x1[l]],[x2[l-1],x2[l]],color='white',lw=3,alpha = max((l-(L-20))/20,0),zorder=3)\n",
    "            if l == L-1:\n",
    "                #plt.plot([x1[l],x1[l]],[-3,3],color='blue',alpha=0.7,zorder=10)\n",
    "                pdf = norm.pdf(x, loc=lmu, scale=lstd)*0.5\n",
    "                mask = pdf > np.percentile(pdf,q=40)\n",
    "                plt.fill_betweenx(x[mask],x1[l]+pdf[mask],np.full(len(x[mask]),x1[l]),color='blue',alpha=0.2,zorder=2)\n",
    "                plt.plot(x1[l]+pdf[mask],x[mask],color='blue',alpha=0.7,zorder=1)\n",
    "                plt.arrow(x1[l-1],x2[l-1],0,x2[l]-x2[l-1],color='black',lw=0.5,head_width=0.05,length_includes_head=True,zorder=100)\n",
    "                plt.scatter(x1[l],x2[l],color='white',edgecolor='blue',s=30,linewidth=1,alpha=1.0,zorder=100)\n",
    "                plt.annotate(r'$f_{X_2|X_1}$ = ' + str(np.round(x1[l],2)),xy=[x1[l]+0.02,max(x[mask])-0.2],color='blue',rotation=-90)\n",
    "        elif case == 1: # update x1\n",
    "            x2[l] = x2[l-1]\n",
    "            lmu = mu1 + rho * (sig1/sig2) * (x2[l] - mu2); lstd = 1 - rho**2\n",
    "            x1[l] = np.random.normal(loc = lmu,scale = lstd,size = 1)\n",
    "            case = 0\n",
    "            plt.scatter(x1[l],x2[l],color='red',edgecolor='black',s=15,alpha=1.0,zorder=100)\n",
    "            plt.plot([x1[l-1],x1[l]],[x2[l-1],x2[l]],color='black',lw=1,alpha = max((l-(L-20))/20,0),zorder=4)\n",
    "            plt.plot([x1[l-1],x1[l]],[x2[l-1],x2[l]],color='white',lw=3,alpha = max((l-(L-20))/20,0),zorder=3)\n",
    "            if l == L-1:\n",
    "                #plt.plot([-3,3],[x2[l],x2[l]],color='red',alpha=0.7,zorder=10)\n",
    "                pdf = norm.pdf(x, loc=lmu, scale=lstd)*0.5\n",
    "                mask = pdf > np.percentile(pdf,q=40)\n",
    "                plt.fill_between(x[mask],x2[l]+pdf[mask],np.full(len(x[mask]),x2[l]),color='red',alpha=0.2,zorder=2)\n",
    "                plt.plot(x[mask],x2[l]+pdf[mask],color='red',alpha=0.7,zorder=1)\n",
    "                plt.arrow(x1[l-1],x2[l-1],x1[l]-x1[l-1],0,color='black',lw=0.5,head_width=0.05,length_includes_head=True,zorder=100)\n",
    "                plt.scatter(x1[l],x2[l],color='white',edgecolor='red',s=30,linewidth=1,alpha=1.0,zorder=100)\n",
    "                plt.annotate(r'$f_{X_1|X_2}$ = ' + str(np.round(x2[l],2)),xy=[min(x[mask])-0.5,x2[l]+0.1],color='red')\n",
    "    \n",
    "    df = pd.DataFrame(np.vstack([x1,x2]).T, columns= ['x1','x2'])\n",
    "    if L > 20:\n",
    "        sns.kdeplot(data=df,x='x1',y='x2',color='grey',linewidths=1.0,alpha=min(((l-20)/20),1.0),levels=5,zorder=1)\n",
    "    add_grid()\n",
    "    plt.xlim([-3.5,3.5]); plt.ylim([-3.5,3.5]); plt.xlabel(r'$X_1$'); plt.ylabel(r'$X_2$'); plt.title('Gibbs Sampler - Bivariate Joint Gaussian Distribution')\n",
    "    plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size \n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(run_plot, {'nsample':nsample,'rho':rho})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faaceed1",
   "metadata": {},
   "source": [
    "### Interactive Gibbs Sampler Demonstation \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "Set the number of samples and correlation coefficient and observe the Gibbs sampler.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **$n_{sample}$** - number of samples, **$\\rho_{X_1,X_2}$** - correlation coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "899c4fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "display(ui2, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07eb83a5",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of the Gibbs sampler for McMC. I have many other demonstrations and even basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael J. Pyrcz, Professor, The University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Professor, The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
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